{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "suffering-fitness",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from sklearn import preprocessing\n",
    "flag=0\n",
    "# 读取体素模型的文件\n",
    "def read(path):\n",
    "    result = []\n",
    "    with open(path, 'r') as f:\n",
    "        for line in f.readlines():\n",
    "            temp = []\n",
    "            for a in line.strip().split(','):\n",
    "                temp.append(float(a))\n",
    "            result.append(temp)\n",
    "    # result是[[x,y,z,0],[x1,y1,z1,1],....]的形式，你可以直接读取\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "rental-samoa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def layer_sizes():\n",
    "    n_x = 3  #x,y,z\n",
    "    n_h = 16 #定义隐藏层size\n",
    "    n_y = 4  #a,b,c,d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "interstate-webster",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    #初始化参数\n",
    "    W1 =np.random.rand(n_h,n_x)*0.01\n",
    "    b1 = np.random.rand(n_h,1)*0.01\n",
    "    #print(W1)\n",
    "    #print(b1)\n",
    "    parameters={\"W1\":W1,\"b1\":b1}\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "driving-internet",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_propagation(X, parameters):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    xyz=X[:,:3]\n",
    "    #矩阵乘法，求取距离\n",
    "    Z1 = np.dot(xyz,W1.T) + b1.T  #Z1=AX+BY+CZ+D 返回(3万,3)*(3,16)+(1,16)=(3万，16)\n",
    "    return Z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fourth-basement",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cost(Z1, X, parameters):\n",
    "    '''\n",
    "        第一列表示表面点为正或0的个数，\n",
    "        第二列表示表面点为负的个数,\n",
    "        第三列表示外点为正或0的个数，\n",
    "        第四列表示外点为负的个数。\n",
    "        首先选出表面点不管正负最多的几行，\n",
    "        然后选取外点符号相反数目最多的那个\n",
    "    '''\n",
    "    cnt=np.zeros((16,4)) \n",
    "    #遍历判断在面的哪一侧\n",
    "    for i in range(len(Z1)):\n",
    "        if(X[i][3]>0):\n",
    "            for j in range(len(Z1[i])):\n",
    "                if(Z1[i][j]>=0):\n",
    "                    cnt[j][0]+=1\n",
    "                if(Z1[i][j]<0):\n",
    "                    cnt[j][1]+=1\n",
    "        if(X[i][3]==0):\n",
    "            for j in range(len(Z1[i])):\n",
    "                if(Z1[i][j]>=0):\n",
    "                    cnt[j][2]+=1\n",
    "                if(Z1[i][j]<0):\n",
    "                    cnt[j][3]+=1\n",
    "    #最后要返回一个a,b,c,d的数组\n",
    "    max1=0\n",
    "    max2=0\n",
    "    ret=0\n",
    "    #print(cnt)\n",
    "    #选出内点数最多，外点数最多的点\n",
    "    for i in range(len(cnt)):\n",
    "        if(cnt[i][0]>=max1):\n",
    "            flag=0\n",
    "            if(cnt[i][0]==max1 and cnt[i][3]>max2):\n",
    "                max1=cnt[i][0]\n",
    "                max2=cnt[i][3]\n",
    "                ret=i\n",
    "            if(cnt[i][0]>max1):\n",
    "                max1=cnt[i][0]\n",
    "                max2=cnt[i][3]\n",
    "                ret=i\n",
    "        if(cnt[i][1]>=max1):\n",
    "            flag=1\n",
    "            if(cnt[i][1]==max1 and cnt[i][2]>max2):\n",
    "                max1=cnt[i][1]\n",
    "                max2=cnt[i][2]\n",
    "                ret=i\n",
    "            if(cnt[i][1]>max1):\n",
    "                max1=cnt[i][1]\n",
    "                max2=cnt[i][2]\n",
    "                ret=i\n",
    "    #返回最佳的平面方程\n",
    "    a,b,c=parameters[\"W1\"][ret]\n",
    "    d=parameters[\"b1\"][ret]\n",
    "    cache=[a,b,c,d]\n",
    "    print(\"内点数\",max1,\"外点数\",max2)\n",
    "    print(cache)\n",
    "    return cache,max1,max2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adjustable-costume",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fantastic-thailand",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backward_propagation(cache, X, rate, i):\n",
    "    #每5个epoch学习率下降\n",
    "    rate=rate*0.625**(i//5)\n",
    "    li=[]\n",
    "    cache=np.array(cache)\n",
    "    #添加扰动\n",
    "    for a in [1,-1]:\n",
    "        for b in [1,-1]:\n",
    "            for c in [1,-1]:\n",
    "                for d in [1,-1]:\n",
    "                    temp=[a,b,c,d]\n",
    "                    li.append(temp)\n",
    "    li=np.array(li)*rate\n",
    "    W1=li[:,:3]+cache[:3]\n",
    "    b1=li[:,3]+cache[3]\n",
    "    parameters={\"W1\":W1,\"b1\":b1}\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "downtown-start",
   "metadata": {},
   "outputs": [],
   "source": [
    "def nn_model(X, num_iterations = 1000, print_cost=False):\n",
    "    n_x = 3  #x,y,z\n",
    "    n_h = 16 #定义隐藏层size\n",
    "    n_y = 4  #a,b,c,d\n",
    "    rate=0.1\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    for i in range(num_iterations):\n",
    "        print(\"iteration\",i+1)\n",
    "        Z1 = forward_propagation(X, parameters)\n",
    "        #print(Z1)\n",
    "        cache,max1,max2 = compute_cost(Z1, X, parameters)\n",
    "        parameters = backward_propagation(cache, X, rate,i+1)\n",
    "    return cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "mechanical-importance",
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除坐标点，暂时未用到\n",
    "def move_face(X,cache):\n",
    "    a,b,c,d=cache\n",
    "    W=np.array([[a,b,c,d]])\n",
    "    num=0\n",
    "    Y=np.dot(X[:,:3],W[:,:3].T)+W[:,3].T\n",
    "    if(flag==0):\n",
    "        for i in range(len(X)):\n",
    "            if(X[i][3]==0 and Y[i]<0):\n",
    "                X[i][3]=-0.2\n",
    "                num+=1\n",
    "    if(flag==1):\n",
    "         for i in range(len(X)):\n",
    "            if(X[i][3]==0 and Y[i]>=0):\n",
    "                X[i][3]=-0.2\n",
    "                num+=1\n",
    "    print(\"num:\",num)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "funny-mouth",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "face: 1\n",
      "iteration 1\n",
      "内点数 281.0 外点数 0.0\n",
      "[0.006829930947460045, 0.008376750736026476, 0.006371964840003847, array([0.00155385])]\n",
      "iteration 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-09e1bf7c416b>:4: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  cache=np.array(cache)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "内点数 281.0 外点数 2336.0\n",
      "[-0.09317006905253997, -0.09162324926397353, -0.09362803515999615, 0.10155385288318484]\n",
      "iteration 3\n",
      "内点数 281.0 外点数 2034.0\n",
      "[-0.19317006905253997, -0.19162324926397353, -0.19362803515999616, 0.20155385288318484]\n",
      "iteration 4\n",
      "内点数 281.0 外点数 2336.0\n",
      "[-0.09317006905253997, -0.09162324926397353, -0.09362803515999615, 0.10155385288318483]\n",
      "iteration 5\n",
      "内点数 281.0 外点数 2034.0\n",
      "[-0.19317006905253997, -0.19162324926397353, -0.19362803515999616, 0.20155385288318484]\n",
      "iteration 6\n",
      "内点数 281.0 外点数 2184.0\n",
      "[-0.13067006905253997, -0.12912324926397353, -0.13112803515999616, 0.13905385288318484]\n",
      "iteration 7\n",
      "内点数 281.0 外点数 2034.0\n",
      "[-0.19317006905253997, -0.19162324926397353, -0.19362803515999616, 0.20155385288318484]\n",
      "iteration 8\n",
      "内点数 281.0 外点数 2184.0\n",
      "[-0.13067006905253997, -0.12912324926397353, -0.13112803515999616, 0.13905385288318484]\n",
      "iteration 9\n",
      "内点数 281.0 外点数 2034.0\n",
      "[-0.19317006905253997, -0.19162324926397353, -0.19362803515999616, 0.20155385288318484]\n",
      "iteration 10\n",
      "内点数 281.0 外点数 2184.0\n",
      "[-0.13067006905253997, -0.12912324926397353, -0.13112803515999616, 0.13905385288318484]\n",
      "iteration 11\n",
      "内点数 281.0 外点数 2345.0\n",
      "[-0.09160756905253997, -0.09006074926397353, -0.09206553515999616, 0.09999135288318484]\n",
      "iteration 12\n",
      "内点数 281.0 外点数 2184.0\n",
      "[-0.13067006905253997, -0.12912324926397353, -0.13112803515999616, 0.13905385288318484]\n",
      "iteration 13\n",
      "内点数 281.0 外点数 2345.0\n",
      "[-0.09160756905253997, -0.09006074926397353, -0.09206553515999616, 0.09999135288318484]\n",
      "iteration 14\n",
      "内点数 281.0 外点数 2184.0\n",
      "[-0.13067006905253997, -0.12912324926397353, -0.13112803515999616, 0.13905385288318484]\n",
      "iteration 15\n",
      "内点数 281.0 外点数 2345.0\n",
      "[-0.09160756905253997, -0.09006074926397353, -0.09206553515999616, 0.09999135288318484]\n",
      "iteration 16\n",
      "内点数 281.0 外点数 2238.0\n",
      "[-0.11602163155253997, -0.11447481176397353, -0.11647959765999616, 0.12440541538318484]\n",
      "iteration 17\n",
      "内点数 281.0 外点数 2345.0\n",
      "[-0.09160756905253997, -0.09006074926397353, -0.09206553515999616, 0.09999135288318484]\n",
      "iteration 18\n",
      "内点数 281.0 外点数 2238.0\n",
      "[-0.11602163155253997, -0.11447481176397353, -0.11647959765999616, 0.12440541538318484]\n",
      "iteration 19\n",
      "内点数 281.0 外点数 2345.0\n",
      "[-0.09160756905253997, -0.09006074926397353, -0.09206553515999616, 0.09999135288318484]\n",
      "iteration 20\n",
      "内点数 281.0 外点数 2238.0\n",
      "[-0.11602163155253997, -0.11447481176397353, -0.11647959765999616, 0.12440541538318484]\n",
      "iteration 21\n",
      "内点数 281.0 外点数 2299.0\n",
      "[-0.10076284249003997, -0.09921602270147353, -0.10122080859749616, 0.10914662632068484]\n",
      "iteration 22\n",
      "内点数 281.0 外点数 2395.0\n",
      "[-0.08550405342753997, -0.08395723363897353, -0.08596201953499616, 0.09388783725818484]\n",
      "iteration 23\n",
      "内点数 281.0 外点数 2299.0\n",
      "[-0.10076284249003997, -0.09921602270147353, -0.10122080859749616, 0.10914662632068484]\n",
      "iteration 24\n",
      "内点数 281.0 外点数 2395.0\n",
      "[-0.08550405342753997, -0.08395723363897353, -0.08596201953499616, 0.09388783725818484]\n",
      "iteration 25\n",
      "内点数 281.0 外点数 2299.0\n",
      "[-0.10076284249003997, -0.09921602270147353, -0.10122080859749616, 0.10914662632068484]\n",
      "iteration 26\n",
      "内点数 281.0 外点数 2347.0\n",
      "[-0.09122609932597747, -0.08967927953741103, -0.09168406543343366, 0.09960988315662234]\n",
      "iteration 27\n",
      "内点数 281.0 外点数 2421.0\n",
      "[-0.08168935616191497, -0.08014253637334853, -0.08214732226937116, 0.09007313999255984]\n",
      "iteration 28\n",
      "内点数 281.0 外点数 2347.0\n",
      "[-0.09122609932597747, -0.08967927953741103, -0.09168406543343366, 0.09960988315662234]\n",
      "iteration 29\n",
      "内点数 281.0 外点数 2421.0\n",
      "[-0.08168935616191497, -0.08014253637334853, -0.08214732226937116, 0.09007313999255984]\n",
      "iteration 30\n",
      "内点数 281.0 外点数 2347.0\n",
      "[-0.09122609932597747, -0.08967927953741103, -0.09168406543343366, 0.09960988315662234]\n",
      "iteration 31\n",
      "内点数 281.0 外点数 2397.0\n",
      "[-0.08526563484843841, -0.08371881505987197, -0.0857236009558946, 0.09364941867908327]\n",
      "iteration 32\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.07930517037089935, -0.07775835058233291, -0.07976313647835553, 0.08768895420154421]\n",
      "iteration 33\n",
      "内点数 281.0 外点数 2397.0\n",
      "[-0.08526563484843841, -0.08371881505987197, -0.0857236009558946, 0.09364941867908327]\n",
      "iteration 34\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.07930517037089935, -0.07775835058233291, -0.07976313647835553, 0.08768895420154421]\n",
      "iteration 35\n",
      "内点数 281.0 外点数 2397.0\n",
      "[-0.08526563484843841, -0.08371881505987197, -0.0857236009558946, 0.09364941867908327]\n",
      "iteration 36\n",
      "内点数 281.0 外点数 2423.0\n",
      "[-0.0815403445499765, -0.07999352476141006, -0.08199831065743268, 0.08992412838062136]\n",
      "iteration 37\n",
      "内点数 281.0 外点数 2452.0\n",
      "[-0.07781505425151458, -0.07626823446294814, -0.07827302035897077, 0.08619883808215945]\n",
      "iteration 38\n",
      "内点数 281.0 外点数 2423.0\n",
      "[-0.0815403445499765, -0.07999352476141006, -0.08199831065743268, 0.08992412838062136]\n",
      "iteration 39\n",
      "内点数 281.0 外点数 2452.0\n",
      "[-0.07781505425151458, -0.07626823446294814, -0.07827302035897077, 0.08619883808215945]\n",
      "iteration 40\n",
      "内点数 281.0 外点数 2423.0\n",
      "[-0.0815403445499765, -0.07999352476141006, -0.08199831065743268, 0.08992412838062136]\n",
      "iteration 41\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.0792120381134378, -0.07766521832487136, -0.07967000422089399, 0.08759582194408266]\n",
      "iteration 42\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.0768837316768991, -0.07533691188833266, -0.07734169778435529, 0.08526751550754397]\n",
      "iteration 43\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.0792120381134378, -0.07766521832487136, -0.07967000422089399, 0.08759582194408266]\n",
      "iteration 44\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.0768837316768991, -0.07533691188833266, -0.07734169778435529, 0.08526751550754397]\n",
      "iteration 45\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.0792120381134378, -0.07766521832487136, -0.07967000422089399, 0.08759582194408266]\n",
      "iteration 46\n",
      "内点数 281.0 外点数 2453.0\n",
      "[-0.07775684659060111, -0.07621002680203467, -0.0782148126980573, 0.08614063042124598]\n",
      "iteration 47\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.0792120381134378, -0.07766521832487136, -0.07967000422089399, 0.08759582194408266]\n",
      "iteration 48\n",
      "内点数 281.0 外点数 2453.0\n",
      "[-0.07775684659060111, -0.07621002680203467, -0.0782148126980573, 0.08614063042124598]\n",
      "iteration 49\n",
      "内点数 281.0 外点数 2444.0\n",
      "[-0.0792120381134378, -0.07766521832487136, -0.07967000422089399, 0.08759582194408266]\n",
      "iteration 50\n",
      "内点数 281.0 外点数 2453.0\n",
      "[-0.07775684659060111, -0.07621002680203467, -0.0782148126980573, 0.08614063042124598]\n",
      "iteration 51\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.07684735188882819, -0.07530053210026175, -0.07730531799628437, 0.08523113571947305]\n",
      "iteration 52\n",
      "内点数 281.0 外点数 2453.0\n",
      "[-0.07775684659060111, -0.07621002680203467, -0.0782148126980573, 0.08614063042124598]\n",
      "iteration 53\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.07684735188882819, -0.07530053210026175, -0.07730531799628437, 0.08523113571947305]\n",
      "iteration 54\n",
      "内点数 281.0 外点数 2453.0\n",
      "[-0.07775684659060111, -0.07621002680203467, -0.0782148126980573, 0.08614063042124598]\n",
      "iteration 55\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.07684735188882819, -0.07530053210026175, -0.07730531799628437, 0.08523113571947305]\n",
      "iteration 56\n",
      "内点数 281.0 外点数 2456.0\n",
      "[-0.07741578607743627, -0.07586896628886983, -0.07787375218489245, 0.08579956990808113]\n",
      "iteration 57\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.07684735188882819, -0.07530053210026175, -0.07730531799628437, 0.08523113571947305]\n",
      "iteration 58\n",
      "内点数 281.0 外点数 2456.0\n",
      "[-0.07741578607743627, -0.07586896628886983, -0.07787375218489245, 0.08579956990808113]\n",
      "iteration 59\n",
      "内点数 281.0 外点数 2462.0\n",
      "[-0.07684735188882819, -0.07530053210026175, -0.07730531799628437, 0.08523113571947305]\n",
      "iteration 60\n",
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    {
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     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "name": "stdout",
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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      "[0.22205648371306277, 0.208302982136499, 0.17929563930587755, -0.39195798665157605]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
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     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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      "[0.003485350823803307, 0.00999683692539764, 0.007880815534870293, array([0.00648063])]\n",
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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    {
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     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
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     "output_type": "stream",
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    {
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    {
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     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[0.0021292428380300966, 0.007347234582691469, 0.005037981797635078, array([0.0064755])]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "内点数 281.0 外点数 2455.0\n",
      "[-0.08734107923035202, -0.094265032427111, -0.09350564456045647, 0.10167026240209291]\n",
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      "[-0.08734065388122221, -0.09426460707798119, -0.09350531005335828, 0.1016698370529631]\n",
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      "[-0.08734065278645778, -0.09426460598321676, -0.09350531114812272, 0.10166983595819867]\n",
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      "[-0.08734065169169335, -0.09426460488845233, -0.09350531005335828, 0.10166983486343424]\n",
      "iteration 199\n",
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      "[-0.08734065059692891, -0.0942646037936879, -0.09350530895859385, 0.10166983376866981]\n",
      "iteration 200\n",
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      "[-0.08734064950216448, -0.09426460269892346, -0.09350530786382942, 0.10166983267390538]\n"
     ]
    }
   ],
   "source": [
    "li=read('D:/codeFile/Python/facePointTargetXin.txt')\n",
    "X=np.array(li)\n",
    "\n",
    "#归一化操作\n",
    "#min_max_scaler = preprocessing.MinMaxScaler()\n",
    "#X = min_max_scaler.fit_transform(X)\n",
    "#print(X)\n",
    "\n",
    "face=30\n",
    "epoch=200\n",
    "ans=[]\n",
    "for i in range(face):\n",
    "    print(\"face:\",i+1)\n",
    "    cache=nn_model(X,epoch,True)\n",
    "    #X=move_face(X,cache)\n",
    "    ans.append(cache)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "specified-absence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.07617286097361338, -0.07486041130792663, -0.0769060606068583, 0.0846650650215068], [0.20699535564836427, 0.2042354227605994, 0.19779557113940616, -0.3878618569913992], [-0.07105927942431452, -0.06741208116172749, -0.07220984188014702, 0.07791021253373963], [0.4232251754960519, 0.3067922508422607, 0.42651239854432477, -0.4161290606393184], [-0.09239562884825105, -0.09239119745757436, -0.0920217527195165, 0.17587561318572995], [-0.09448664226411375, -0.094149900056112, -0.09352122478352183, 0.17932840269254127], [0.08252500350113263, 0.08066063735658312, -0.0756889832128348, -0.07621596191326264], [-0.08851337655533577, -0.08742091579254622, -0.0886763523225713, 0.0984655643145977], [-0.08799697684728065, 0.09373590667295974, 0.09312359565737623, -0.08664724450238283], [0.22205650136066524, 0.20830299759457263, 0.17929562866476736, -0.3919580021096497], [-0.08829995700612185, -0.08778015743541308, -0.08717772421313143, 0.09790160416008238], [-0.07691452462429542, -0.07460176485010857, -0.07697725838823984, 0.08486504497402615], [-0.08605655428876637, -0.08614911636769769, -0.08349281565212656, 0.09496446349094116], [0.086751271235033, 0.08529190181710077, -0.081740760915389, -0.07937349447095475], [0.4062393617193423, 0.28782424935395623, 0.41042440474743525, -0.3964468556994468], [0.13854730876520238, 0.14178538718743416, -0.11874725726097136, -0.026500247308024465], [-0.06405274210434266, -0.06407400863754899, -0.06321847324155906, 0.07120581171022196], [0.20483856233326136, 0.2047337709775469, 0.2014004739245194, -0.3885162839399743], [-0.08949632771931194, -0.0904825265508948, -0.08835861285531664, 0.09967560481899208], [-0.0885156042693203, -0.09074856104217817, -0.09155462678015537, 0.10050372934890515], [0.10060065367392813, -0.09121498639528057, 0.10152122586387895, -0.09559812583191661], [-0.08369523697645427, -0.08424514418049968, -0.07580319917922478, 0.08981557884361162], [0.42261930326540476, 0.29663602173153714, 0.42849703362871283, -0.41145229667262606], [0.20670305190545618, 0.20656730535809334, 0.20324332866770392, -0.39203649544575436], [0.41573566834039266, 0.2985559794372924, 0.42249573328762435, -0.40835825829538486], [-0.05303621214172769, -0.052395479376500805, -0.053066112219004154, 0.0589897861900115], [0.21457299120450657, 0.20593884533900983, 0.19271910197252076, -0.3921306974842621], [-0.09460685818643082, 0.10462031358231895, 0.09841268597090137, -0.09537807929395085], [0.3836304780621228, 0.263496111270798, 0.38869067608215746, -0.3703677485901008], [-0.08734064950216448, -0.09426460269892346, -0.09350530786382942, 0.10166983267390538]]\n"
     ]
    }
   ],
   "source": [
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "brave-chemistry",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "grave-income",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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